code: def __call__(self, prediction, ground_truth):
      self.num_samples += len(prediction)
      for metric_fn in self.metric_fns:
          for p, gt in zip(prediction, ground_truth):
              res = metric_fn(p, gt)
              dict_key = metric_fn.__name__
              self.result_dict[dict_key] += res
  
file_path: medicaltorch/medicaltorch/metrics.py
imports: from collections import defaultdict
  from scipy import spatial
  import numpy
label_desc: Use image node_id 2 for calling the MetricManager obj's underlying member method code with example usage: obj.__call__(prediction, ground_truth) without return types